Country

Indicator

Value type

Values present

School enrollment, primary, male (% gross)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	97.2	-2.37%	61
Andorra	97.7	+8.18%	55
United Arab Emirates	106	+11.8%	30
Armenia	94.2	+0.79%	72
Azerbaijan	102	+2.2%	40
Burkina Faso	71.1	-12.6%	102
Bangladesh	107	-4.84%	29
Bahrain	93.5	+1.63%	76
Bahamas	78.4	-16.7%	99
Bosnia & Herzegovina	87.5	-1.02%	88
Belarus	95.5	-1.04%	67
Belize	97.5	-3.44%	58
Bermuda	86.3	+1.24%	91
Bolivia	99	-0.588%	51
Barbados	95.1	-2.03%	69
Brunei	93.1	-0.712%	79
China	98.5	-0.941%	52
Côte d’Ivoire	104	+7.37%	36
Cameroon	118	+1.46%	14
Congo - Kinshasa	123	-2.31%	8
Congo - Brazzaville	90	+1.17%	86
Comoros	97.7	+11.9%	56
Cuba	99.1	-1.41%	50
Curaçao	111	-3.43%	20
Cayman Islands	103	+1.26%	38
Dominica	90.6	-3.16%	84
Dominican Republic	96.2	-5.95%	65
Algeria	110	+0.0271%	23
Ecuador	96	-2.2%	66
Egypt	90.1	-1.48%	85
Ethiopia	87.4	-1.52%	89
Fiji	110	-0.422%	22
Georgia	103	-0.863%	37
Gibraltar	121	-2.98%	12
Gambia	87.3	+1.23%	90
Guatemala	104	-1.1%	35
Guyana	99.4	-0.169%	49
Honduras	86	+0.988%	92
Indonesia	102	-0.657%	43
India	113	+1.15%	18
Jamaica	84.4	-7.96%	95
Jordan	98.1	-0.836%	54
Kazakhstan	100	-0.288%	46
Kyrgyzstan	96.7	+1.95%	63
Cambodia	113	+3.41%	17
Kiribati	93.8	-4.55%	73
Laos	97.6	-0.599%	57
Lebanon	80.5	-5.23%	97
St. Lucia	101	-1.89%	45
Lesotho	88.3	-2.58%	87
Macao SAR China	85.5	+2.82%	93
Morocco	116	+0.156%	16
Madagascar	133	-1.98%	5
Maldives	96.7	+2.14%	64
Mali	77.9	+2.51%	100
Montenegro	106	+0.242%	31
Mongolia	95.3	+0.122%	68
Mozambique	122	-1.66%	9
Mauritania	112	+33.7%	19
Mauritius	110	+11.3%	21
Malawi	134	+7.69%	4
Malaysia	98.1	+0.229%	53
Niger	70.5	-0.63%	103
Nicaragua	107	+0.372%	27
Nepal	125	+2.4%	7
Nauru	100	+7.49%	47
Oman	94.7	+6.18%	70
Panama	94.6	-6.33%	71
Peru	107	-0.305%	28
Philippines	93.7	+1.87%	74
Palau	96.8	+8.31%	62
Puerto Rico	83	-18.1%	96
Paraguay	92.8	-1.04%	80
Palestinian Territories	92.4	+0.326%	81
Russia	97.5	-3.67%	59
Rwanda	154	+2.82%	2
Senegal	75.6	-1.22%	101
Solomon Islands	84.9	-12.2%	94
Sierra Leone	149	-2.31%	3
El Salvador	90.9	+2.39%	82
San Marino	93.6	+0.671%	75
Somalia	23.1	-22.3%	104
Suriname	104	+4.23%	34
Eswatini	117	-7.06%	15
Sint Maarten	163		1
Seychelles	97.3	-0.0809%	60
Syria	79.6	+7.01%	98
Turks & Caicos Islands	128	-1.59%	6
Chad	99.6	+0.913%	48
Togo	122	-1.76%	11
Thailand	106	-0.414%	32
Tajikistan	101	-0.619%	44
Timor-Leste	120	+1.53%	13
Tonga	102	-2.61%	41
Trinidad & Tobago	93.2	-5.81%	78
Tunisia	105	-0.327%	33
Tuvalu	102	+2.19%	42
Tanzania	90.7	+5.51%	83
Uzbekistan	93.4	-0.673%	77
St. Vincent & Grenadines	109	-0.631%	24
Venezuela	109	+14.9%	25
Vietnam	122	-0.517%	10
Vanuatu	108	-4%	26
Samoa	102	-1.88%	39

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SE.PRM.ENRR.MA'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SE.PRM.ENRR.MA'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

School enrollment, primary, male (% gross)

Year: 2023

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